Multimodal machine learning aims to build models that can process and relate information from multiple modalities. Compared to single modality machine learning techniques, learning from multimodal sources offers the possibility of capturing correspondences between modalities, reducing redundancies, and improving generalisation. Traditional multimodal machine learning approaches have included co-training algorithms [1]}, [2]}, [3]}, co-regularisation algorithms [4]}, [5]}, margin consistency algorithms [6]}, [7]} and multiple kernel learning (MKL) [8]}.
